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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/75065
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dc.contributor.authorMartins, Cesar H.-
dc.contributor.authorAguiar, Paulo R.-
dc.contributor.authorBianchi, Eduardo C.-
dc.contributor.authorFrech, Arminio-
dc.contributor.authorRuzzi, Rodrigo S.-
dc.date.accessioned2014-05-27T11:28:51Z-
dc.date.accessioned2016-10-25T18:46:50Z-
dc.date.available2014-05-27T11:28:51Z-
dc.date.available2016-10-25T18:46:50Z-
dc.date.issued2013-04-03-
dc.identifierhttp://dx.doi.org/10.2316/P.2013.793-015-
dc.identifier.citationIASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013, p. 70-74.-
dc.identifier.urihttp://hdl.handle.net/11449/75065-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/75065-
dc.description.abstractGrinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.en
dc.format.extent70-74-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectAcoustic emission-
dc.subjectDresser wear-
dc.subjectDressing operation-
dc.subjectKohonen neural network-
dc.subjectMultilayer perceptron-
dc.subjectNeural network-
dc.subjectAcoustic emission signal-
dc.subjectFinishing process-
dc.subjectGrinding operations-
dc.subjectHarmonic contents-
dc.subjectIts efficiencies-
dc.subjectKohonen network-
dc.subjectKohonen neural networks-
dc.subjectMulti layer perceptron-
dc.subjectAcoustic emissions-
dc.subjectGrinding wheels-
dc.subjectIntelligent systems-
dc.subjectNeural networks-
dc.subjectGrinding (machining)-
dc.titleApplication of MLP and Kohonen networks for recognition of wear patterns of single-point dressersen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationElectrical/Mechanical Departments FEB - Faculty of Engineering Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01, Vargem Limpa, CEP 17033-360, Bauru, Sao Paulo-
dc.description.affiliationUnespElectrical/Mechanical Departments FEB - Faculty of Engineering Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01, Vargem Limpa, CEP 17033-360, Bauru, Sao Paulo-
dc.identifier.doi10.2316/P.2013.793-015-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofIASTED Multiconferences - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2013-
dc.identifier.scopus2-s2.0-84875495956-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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